Chapter 15 revisions
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@ -110,17 +110,17 @@ ALTER TABLE farmers_markets ADD COLUMN geog_point geography(POINT,4326);
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-- Now fill that column with the lat/long
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UPDATE farmers_markets
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SET geog_point = ST_SetSRID(
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ST_MakePoint(longitude,latitude),4326
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)::geography;
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ST_MakePoint(longitude,latitude)::geography,4326
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);
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-- Add a GiST index
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-- Add a spatial (R-Tree) index using GIST
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CREATE INDEX market_pts_idx ON farmers_markets USING GIST (geog_point);
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-- View the geography column
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SELECT longitude,
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latitude,
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geog_point,
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ST_AsText(geog_point)
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ST_AsEWKT(geog_point)
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FROM farmers_markets
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WHERE longitude IS NOT NULL
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LIMIT 5;
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@ -153,7 +153,7 @@ SELECT market_name,
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round(
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(ST_Distance(geog_point,
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ST_GeogFromText('POINT(-93.6204386 41.5853202)')
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) / 1609.344)::numeric(8,5), 2
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) / 1609.344)::numeric, 2
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) AS miles_from_dt
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FROM farmers_markets
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WHERE ST_DWithin(geog_point,
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@ -161,6 +161,19 @@ WHERE ST_DWithin(geog_point,
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10000)
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ORDER BY miles_from_dt ASC;
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-- Listing 15-13: Using the <-> distance operator for a nearest neighbors search
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SELECT market_name,
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city,
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st,
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round(
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(ST_Distance(geog_point,
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ST_GeogFromText('POINT(-68.2041607 44.3876414)')
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) / 1609.344)::numeric, 2
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) AS miles_from_bh
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FROM farmers_markets
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ORDER BY geog_point <-> ST_GeogFromText('POINT(-68.2041607 44.3876414)')
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LIMIT 3;
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-- WORKING WITH SHAPEFILES
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@ -174,14 +187,14 @@ ORDER BY miles_from_dt ASC;
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-- Import (for use on command line if on macOS or Linux; see Chapter 18)
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shp2pgsql -I -s 4269 -W LATIN1 tl_2019_us_county.shp us_counties_2019_shp | psql -d analysis -U postgres
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-- Listing 15-13: Checking the geom column's well-known text representation
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-- Listing 15-14: Checking the geom column's well-known text representation
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SELECT ST_AsText(geom)
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FROM us_counties_2019_shp
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ORDER BY gid
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LIMIT 1;
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-- Listing 15-14: Find the largest counties by area using ST_Area()
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-- Listing 15-15: Find the largest counties by area using ST_Area()
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SELECT name,
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statefp AS st,
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@ -192,7 +205,7 @@ FROM us_counties_2019_shp
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ORDER BY square_miles DESC
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LIMIT 5;
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-- Listing 15-15: Using ST_Within() to find the county belonging to a pair of coordinates
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-- Listing 15-16: Using ST_Within() to find the county belonging to a pair of coordinates
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SELECT sh.name,
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c.state_name
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@ -202,8 +215,7 @@ WHERE ST_Within(
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'SRID=4269;POINT(-118.3419063 34.0977076)'::geometry, geom
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);
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-- Listing 15-16: Using ST_DWithin() to count people near Lincoln, Nebraska
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-- Listing 15-17: Using ST_DWithin() to count people near Lincoln, Nebraska
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SELECT sum(c.pop_est_2019) AS pop_est_2019
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FROM us_counties_2019_shp sh JOIN us_counties_pop_est_2019 c
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ON sh.statefp = c.state_fips AND sh.countyfp = c.county_fips
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@ -211,7 +223,11 @@ WHERE ST_DWithin(sh.geom::geography,
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ST_GeogFromText('SRID=4269;POINT(-96.699656 40.811567)'),
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80467);
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-- Listing 15-17: Displaying counties near Lincoln, Nebraska
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-- Note: You can speed up the above query by creating a functional index
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-- that covers the casting of the geom column to a geography type.
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CREATE INDEX us_counties_2019_shp_geog_idx ON us_counties_2019_shp USING GIST (CAST(geom AS geography));
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-- Listing 15-18: Displaying counties near Lincoln, Nebraska
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SELECT sh.name,
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c.state_name,
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c.pop_est_2019,
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@ -250,7 +266,7 @@ WHERE ST_DWithin(sh.geom::geography,
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shp2pgsql -I -s 4269 -W LATIN1 tl_2019_35049_linearwater.shp santafe_linearwater_2019 | psql -d analysis -U postgres
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shp2pgsql -I -s 4269 -W LATIN1 tl_2019_35049_roads.shp santafe_roads_2019 | psql -d analysis -U postgres
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-- Listing 15-18: Using ST_GeometryType() to determine geometry
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-- Listing 15-19: Using ST_GeometryType() to determine geometry
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SELECT ST_GeometryType(geom)
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FROM santafe_linearwater_2019
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@ -260,7 +276,7 @@ SELECT ST_GeometryType(geom)
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FROM santafe_roads_2019
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LIMIT 1;
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-- Listing 15-19: Spatial join with ST_Intersects() to find roads crossing the Santa Fe river
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-- Listing 15-20: Spatial join with ST_Intersects() to find roads crossing the Santa Fe river
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SELECT water.fullname AS waterway,
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roads.rttyp,
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@ -280,7 +296,7 @@ WHERE water.fullname = 'Santa Fe Riv'
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AND roads.fullname IS NOT NULL
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ORDER BY roads.fullname;
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-- Listing 15-20: Using ST_Intersection() to show where roads cross the river
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-- Listing 15-21: Using ST_Intersection() to show where roads cross the river
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SELECT water.fullname AS waterway,
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roads.rttyp,
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@ -290,6 +306,5 @@ FROM santafe_linearwater_2019 water JOIN santafe_roads_2019 roads
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ON ST_Intersects(water.geom, roads.geom)
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WHERE water.fullname = 'Santa Fe Riv'
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AND roads.fullname IS NOT NULL
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ORDER BY roads.fullname
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LIMIT 5;
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ORDER BY roads.fullname;
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@ -25,7 +25,7 @@ FMID,MarketName,street,city,County,State,zip,longitude,latitude,Organic
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1005299,29th and Wharton Farmers' Market,29th and Wharton Streets,Philadelphia,Philadelphia,Pennsylvania,19146,-75.19204,39.9373,N
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1010994,2nd Street Farmers' Market,194 second street,Amherst,Amherst,Virginia,24521,-79.048573,37.583311,N
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1009959,2nd Street Market - Five Rivers MetroPark,600 E. 2nd Street,Dayton,Montgomery,Ohio,45402,-84.181030,39.762593,Y
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1004950,3 French Hens French Country Market,123 W. Illinois ave. ,Morris,Grundy,Illinois,60450,-88.425186,41.356383,Y
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1004950,3 French Hens French Country Market,123 W. Illinois Ave.,Morris,Grundy,Illinois,60450,-88.425186,41.356383,Y
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1010775,30A Farmers' Market,Rosmary Beach Town Center,Rosemary Beach,Walton,Florida,32461,-86.002754,30.279017,-
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1012342,31 & Main Farmers Market at Campus Town,1928 Pennington Road,Ewing,Mercer,New Jersey,08618,-74.783264,40.267025,Y
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1005636,32nd Street/Waverly Farmers Market,E. 32nd & Barclay Street,Baltimore,Baltimore,Maryland,21218,-76.611,39.3272,Y
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@ -298,7 +298,7 @@ FMID,MarketName,street,city,County,State,zip,longitude,latitude,Organic
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1006964,Ashley Farmers Market,"On East Main Street,",Ashley,McIntosh County,North Dakota,,-99.3684,46.0344,-
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1000836,Ashley Valley Farmers' Market (Vernal),Old Dinosaur Gardens on Main Street (~240East),Vernal,Uintah,Utah,,-109.524091666667,40.4557472222222,-
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1008599,Ashmont/Peabody Square Farmers Market,Ashmont MBTA Plaza,Dorchester,Suffolk,Massachusetts,02124,-71.064236,42.285528,-
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1004530,Ashotn Farmers Market,Off Main Street,Ashton,Fremont,Idaho,83420,-111.446,44.0714,-
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1004530,Ashton Farmers Market,Off Main Street,Ashton,Fremont,Idaho,83420,-111.446,44.0714,-
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1009244,Ashtabula Farmers Market,1119 Bridge Street,Ashtabula,,Ohio,44004,-80.7998,41.8995,-
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1004692,Aspen Grove Farmers' Market,7301 S Santa Fe Dr,Littleton,Arapahoe,Colorado,80120,-105.025219,39.585505,Y
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1005837,Aspen Saturday Market,East Hopkins and South Hunter,Aspen,Pitkin,Colorado,81611,-106.817,39.1894,-
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Can't render this file because it is too large.
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@ -1009,9 +1009,34 @@ FROM farmers_markets markets JOIN us_counties_2019_shp census
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WHERE markets.county IS NULL
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ORDER BY census.statefp, census.name;
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-- Note that this query also highlights a farmer's market that is mis-geocoded.
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-- Note that the query returns 496 rows; not all of the columns missing a county
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-- value have values present in the geog_point, latitude, and longitude columns.
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-- Also, this query highlights a farmer's market that is mis-geocoded.
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-- Can you spot it?
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-- 4. The nyc_yellow_taxi_trips table you created in Chapter 12 contains
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-- the longitude and latitude where each trip began and ended. Use PostGIS
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-- functions to turn the dropoff coordinates into a geometry type and
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-- count the state/county pairs where each drop-off occurred. As with the
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-- previous exercise, you’ll need to join to the us_counties_2019_shp table
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-- and use its geom column for the spatial join.
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-- Answer:
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SELECT census.statefp,
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census.name as county,
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count(*) AS dropoffs
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FROM nyc_yellow_taxi_trips taxi JOIN us_counties_2019_shp census
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ON ST_Within(
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ST_SetSRID(ST_MakePoint(taxi.dropoff_longitude, taxi.dropoff_latitude),4269)::geometry, census.geom
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)
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GROUP BY census.statefp, census.name
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ORDER BY count(*) DESC;
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-- Thank you to reader Eric Mortenson for suggesting this exercise.
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----------------------------------------------------------------------------
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-- Chapter 16: Working with JSON Data
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----------------------------------------------------------------------------
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